Finally, skip connections are introduced to compensate for crucial information loss caused by the memory modules. Experimental results show that the proposed method outperforms state-of-the-art methods, achieving AUC scores of 99.9%, 94.8%, and 78.9% on the UCSD Ped2, CUHK Avenue, and Shang...
To improve the sharpness of the reconstruction, we consider an autoencoder architecture with skip connections. In the common scenario where only clean images are available for training, we propose to corrupt them with a synthetic noise model to prevent the convergence of the network towards the ...
PyTorch implementation of Ladder Variational Autoencoders (LVAE) [1]: where the variational distributions q at each layer are multivariate Normal with diagonal covariance. Significant differences from [1] include: skip connections in the generative path: conditioning on all layers above rather than on...
To point out the problem of exploding and vanishing gradient problem, a variation of SA-AR that has skip connections between layers is proposed that is called SA-AE-Skip. With these connections, information can smoothly flow across the several layers during the training. Using the proposed ...
PhotoWCT2: Compact Autoencoder for Photorealistic Style Transfer Resulting from Blockwise Training and Skip Connections of High-Frequency Residuals - chiutaiyin/PhotoWCT2
"Image restoration using convolutional auto-encoders with symmetric skip connections." arXiv preprint arXiv:1606.08921 (2016). Google Scholar 14 Ansari M.A., Singh D.K. "Review of Deep Learning Techniques for Object Detection and Classification." In: Verma S., Tomar R., Chaurasia B., Singh...
In [14], the authors prevent the model from learning an identity mapping by introducing skip-connections to Autoencoder-based approaches for Unsupervised Anomaly Detection (UAD) in brain MRI. Using autoencoders for biomedical images would be ideal except for a few data distribution problems like ...
The architecture somewhat resembles U-Net, an encoder-decoder network with skip connections between mirrored layers in the encoder and decoder stacks. The network consists of convolutional, dropout and batch normalization layers LeakyReLU used as the activation function. One of notable differences between...
• Energy disaggregation is performed using a variationalautoencodersframework. • Skip connections have been introduced to the NILM model to enhance load reconstruction performance. • The performance has been evaluated on two public datasets (e.g., UK-DALE and REFIT). ...
By multi-scale token propagation, the lightweight decoder gradually upsamples point tokens with complementary skip connections from the encoder, which further promotes the reconstruction from a global-to-local perspective. Extensive experiments demonstrate the state-of-the-art performance of Point-M2AE ...